Commercial enterprises typically seek to improve financial performance as time goes on. One way to gain insight into possible performance improvements is by analyzing past performance. Business intelligence systems can facilitate this process by synthesizing mountains of data (such as revenue data, cost data, and the like) to allow enterprises to see patterns and gain insights that are not readily apparent from the raw data (often because of the sheer volume of the data to be analyzed).
In particular, data on revenue from sales of products (which can include any of a variety of difference goods and/or services) can be used to drive decisions on which products should be emphasized in marketing campaigns, which products should be developed further, and/or which products should be dropped from an enterprise's product line altogether, to name a few examples. More specifically, past revenue data can provide insight into products that might have a symbiotic relationship (such that they should be marketed together and/or to the same potential customers), as well as insight into geographical trends (which can inform decisions about deployment of sales force resources, marketing resources, and/or the like).
Similarly, an enterprise often will engage in a merger or acquisition of another enterprise with a goal of integrating the product lines of the two enterprises; it is often hoped that this integration will result in a symbiotic relationship between the respective product lines. Merely by way of example, a corporation selling a primary product might acquire another corporation selling a complementary product with the goal of expanding the market of the complementary product and/or with the hope that sales of the complementary product will drive further sales of the primary product. To evaluate the efficacy of such an acquisition retrospectively (and, correspondingly, to learn from that experience in order to inform future acquisition strategies), it would be helpful to be able to analyze the revenue performance of the primary and/or complementary product. Ideally, such analysis could be normalized (to account for other product lines which might also affect revenue performance, etc.).
There are, however, obstacles to the use of revenue data in this fashion. The first obstacle is in identifying patterns in the data. In the past, identification of such patterns has been a difficult and labor-intensive process, requiring personnel with both the data processing expertise to manipulate the data and the business expertise to understand how the data should be manipulated and/or understood. Business intelligence systems have remedied this problem somewhat, but the use of such systems typically still requires significant data processing expertise, often placing them out of reach of the business people who can understand the meaning of identified trends and make business decisions thereupon.
Another obstacle to the employment of revenue data for decision support purposes is that, even if the data can be analyzed and trends identified, it is difficult to convey these trends in a meaningful manner. Research shows that most humans intuitively can grasp information more readily which it is provided in a graphical format than when the information is provided numerically or textually. In some cases, the graphical presentation of data is a trivial matter—for example, a chronological series of numbers can easily be displayed as a line plot or a bar chart, with the horizontal axis representing a time scale. But when such data is multidimensional, it becomes more difficult to convey that data graphically in a meaningful way.
This problem is especially true when the display of the data is meant to provide geographical insight. For example, while applications such as Google Earth™ can be used to map relatively simple, one dimensional data onto a geographical display (such as a world map), this task becomes significantly more complex when the data has no explicit geographical dimension, or when attempting to map multiple data sets with disparate geographical dimensions.
Hence, there is a need for improved tools and techniques for analyzing data (in particular revenue data), and providing the results of that analysis in a meaningful way.